Query-driven Repair of Functional Dependency Violations
Data cleaning is a time-consuming process that depends on the data analysis that users perform. Existing solutions treat data cleaning as a separate offline process that takes place before analysis begins. Applying data cleaning before analysis assumes a priori knowledge of the inconsistencies and the query workload, thereby requiring effort on understanding and cleaning the data that is unnecessary for the analysis.
We propose an approach that performs probabilistic repair of functional dependency violations on-demand, driven by the exploratory analysis that users perform. We introduce Daisy, a system that seamlessly integrates data cleaning into the analysis by relaxing query results. Daisy executes analytical query-workloads over dirty data by weaving cleaning operators into the query plan. Our evaluation shows that Daisy adapts to the workload and outperforms traditional offfine cleaning on both synthetic and real-world workloads.
WOS:000584252700190
2020-01-01
978-1-7281-2903-7
Los Alamitos
IEEE International Conference on Data Engineering
1894
1897
REVIEWED
Event name | Event place | Event date |
Dallas, TX | Apr 20-24, 2020 | |